Page 231 - Artificial Intelligence for the Internet of Everything

212 Artificial Intelligence for the Internet of Everything

relatively high since if a logical flaw in an argument is established, most likely
the whole claim is invalid because other factors besides argumentation (such
as false facts) contribute as well. As the complexity of a complaint and its DT
grows, F1 first improves, since more logical terms are available, and then
goes back down, as there is a higher chance of a reasoning error due to a
noisier input.
For decision support systems it is important to maintain a low false-
positive rate. It is acceptable to miss invalid complaints, but for a detected
invalid complaint, confidence should be rather high. If a human agent is
recommended to look at a given complaint as invalid, his/her expectations
should be met most of the time. Although F1-measure of the overall argu-
ment detection and validation system is low in comparison with modern
recognition systems, it is still believed to be usable as a component of a
CRM decision-support system.

11.8 CONCLUSIONS

In this study we explored the possibility of validating messages in an IoE
environment. We observed that by relying on DT data one can reliably
detect patterns of logical and affective argumentation. CDTs become a
source of information to form a defeasible logic program to validate an argu-
mentation structure. Although the performance of the former being about
80% is significantly above that of the latter (29%), the overall pipeline can be
useful for detecting cases of invalid affective argumentation, which is impor-
tant in decision support for CRM.
To the best of our knowledge this is the first study building a whole argu-
ment validity pipeline including everything from text to a validated claim,
which is a basis of IoE decision support. Hence, although the overall argu-
ment validation accuracy is fairly low, there is no existing system to compare
this performance against.
In this chapter to support IoE message validation we attempted to com-
bine the best of both worlds: argumentation mining from text and reasoning
about the extracted argument. Whereas applications of either technology are
limited, the whole argumentation pipeline is expected to find a broad range
of applications. In this work we focused on a very specific legal area such as
customer complaints, but it is easy to see a decision support system employ-
ing the proposed argumentation pipeline in other domains of CRM.
Message validation is essential not only in decision making, but also in
security IoT applications. Computational models of message validation